2. D.istributed clustering algorithm-ppt - UOIT.CA

A Distributed Clustering Algorithm for
Target Tracking in Vehicular Ad
-
Hoc
Networks


Dr.
Khalil

El
-
Khatib
, Dr. Richard
Pazzi
,
Sanaz

Khakpour


Table of Contents





-

Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Introduction


•
VANETs

are

network

of

autonomous

mobile

nodes

that

communicate

with

each

other

without

any

fixed

infrastructure
.



•
VANETs

are

large
-
scale

networks

and

dividing

the

network

into

smaller

clusters

in

such

dynamic

environment

is

an

advantageous

technic
.


Related Work


•
MANET and WSN clustering algorithms do not work
properly in VANET environment.



•
The most important challenge in clustering algorithms
that most of the protocols are trying to solve are:



Optimal cluster management in VANET’s highly
dynamic environment.


Increasing cluster stability (MDMAC, SBCA)


Prevention of frequent cluster changes


Increasing cluster head availability (SBCA)


Increasing cluster lifetime by using appropriate
mobility metrics (DCA, MDCAM, DMAC, SBCA,
MOBIC, …)


Special features of the proposed algorithm


•
A cluster
-
based target tracking algorithm

•
high
cluster head and cluster lifetime

•

robust and stable clusters

•

low
delay and overhead for electing new cluster
head in lost CH scenarios

•

distributed cluster head selection
mechanism


Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion






Assumptions and Definitions

•
The

proposed

clustering

algorithm

was

designed

for

vehicle

tracking

in

VANETs
.



•
This

algorithm

assumes

that

vehicles

have

front

and

rear

cameras

and

can

detect

visual

features

of

a

target
.


•
A

central

entity

such

as

a

police

station

is

seeking

help

to

find

a

specific

target
.

This

entity

is

called

Control

Centre

(CC)

and

is

a

node

located

in

multi
-
hop

communication

distance

from

target
.







Tracking Failure Probability Metric (TFP)

Assumptions
:


•
All

vehicles

are

aware

of

their

location

and

velocity

by

using

a

GPS

device
.



•
The

location

of

a

target

is

unknown
;

But

can

be

calculated

by

visual

processing
.



•
To

calculate

TFP

between

a

vehicle

A

and

the

target

vehicle

T

at

time

t,

we

need

to

have

the

distance

between

node

A

and

T

and

their

speed

vector

at

that

time
.



•
We

define

a

value

called

Valid

Distance

Range

(VDR),

which

is

used

to

normalize

the

distance

between

any

node

and

target
.







Tracking Failure Probability Metric (TFP)

•
The

normalized

distance

is

calculated

as

follow
:


(
1
)














𝐷
𝐴
𝑁𝑡

=

𝐷
𝐴𝑇
𝑡
𝑉𝐷𝑅


•
By

using

velocity

vectors

of

vehicles

we

can

differentiate

between

nodes

moving

in

the

same

direction

and

nodes

moving

in

opposite

direction
.

𝑉

𝐴
𝑡

is

defined

as
:


(
2
)

𝑉

𝐴
𝑡

=

𝑉
𝐴
𝑡
𝑐𝑜
𝜃


•

We

use

a

value

called

Valid

Velocity

Range

(VVR)

in

order

to

normalize

the

value

of

velocity

vectors
.







Tracking Failure Probability Metric (TFP)

•
V

and

V

Are

normalized

velocity

vectors

of

vehicle

A

and

target

T

respectively
.


(
3
)

𝑉

𝐴
𝑁𝑡
=

𝑉

𝐴
𝑡
𝑉𝑉𝑅

(
4
)

𝑉

𝑇
𝑁𝑡
=

𝑉

𝑇
𝑡
𝑉𝑉𝑅


•
Two

values

α

and

β

are

defined

as

Distance

and

speed

Efficiency

Factors
.

These

values

are

coefficients

of

distance

and

velocity

to

control

efficiency

of

these

metrics

of

each

vehicle
.



•
The

TFP

of

node

A

at

time

t

is

calculated

as

in

the

following

formula
.

The

lower

TFP

indicates

higher

priority

to

become

cluster

head
.



(
5
)


𝑇𝐹𝑃

(
𝐴
)
𝑡
=

100

*

(
𝛼
𝐷
𝐴
𝑁𝑡

+

β

𝑉

𝐴
𝑁𝑡
−

𝑉

𝑇
𝑁𝑡
)


Table of Contents

-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Control Centre Functions

-

Initialization Phase

-

Cluster Management Phase:

o

Cluster Head Functions

o

Cluster Members Functions

-

Tracking Phase


-

Conclusion

Control Centre (CC)

•
CC broadcasts a “Target Tracking Request Message”
(TTRM) to the entire network with target vehicle’s visual
information.


•
When CC receives “Response Message” from any
vehicle that has located the target, it will stop
broadcasting.


•
At any point later, if the CC stops receiving any
information from the cluster head regarding the
specified target (after a pre
-
defined time interval) it will
assume the cluster no longer exists and it will start
broadcasting target’s information again in the network.

Initialization Phase

•
Any

vehicle

that

receives

a

TTRM

message

from

the

Control

Center

(CC)

and

which

can

detect

the

target

responds

to

CC

and

starts

the

initialization

process
.


•
OBNs start broadcasting “Request Messages” to their
neighbors

and receive “Response Messages” from
them. OBNs also check the TDV field of the response
messages.


•
OBNs calculate their TFP. This value displays which
vehicle has closer movement pattern to target and is
more appropriate to be the cluster head.

Initialization Phase

•
Cluster

members

are

divided

into

2

groups
:

level
-
1

(OBNs)

and

Level
-
2

(NNs)
.



•
Member

nodes

are

connected

to

cluster

instead

of

cluster

head
.



•
Initialization

phase

might

be

repeated

only

if

there

is

not

any

cluster

members

available

and

the

cluster

is

destroyed
.



•
The

purpose

of

our

design

is

to

avoid

switching

to

Initialization

Phase

from

Cluster

Maintenance

phase
.


•
After

this

phase

the

initial

cluster

is

created

and

the

cluster

head

is

selected
.



Cluster Maintenance Phase

(Cluster Head Functions)

•
CH

is

responsible

of

managing

the

cluster

by

sending

request

messages

at

every

time

intervals

to

find

new

cluster

members
.


•
the

cluster

head

calculates

its

own

TFP

every,

and

compares

it

with

other

values

in

the

neighbour

list

to

check

if

it

is

still

a

valid

CH
.

If

not

it

will

send

a

“Resign

Message”
.


•
A

“Safe

Threshold”

is

defined

because

the

TFPs

are

changing

so

quickly

and

we

do

not

want

to

change

CH

so

frequently
.


•
vehicles

moving

in

opposite

direction

of

the

target

are

not

supposed

to

join

cluster

because

these

nodes

are

unstable

cluster

members

and

will

decrease

cluster’s

stability
.

Cluster Maintenance Phase

(Cluster Members Functions)

•
OBNs

calculate

their

TFP

every



𝑝

time

interval

and

send

it

in

RPM

to

their

neighbors
.

Also,

OBNs

store

the

TFP

of

other

nodes

in

their

neighbor

list
.


•
If

member

nodes

receive

a

RSM

they

are

responsible

to

find

a

node

with

lowest

TFP

value

in

their

neighbor

list

and

select

it

as

CH
.



•
Also,

if

a

member

node

does

not

receive

any

kind

of

message

after

a

defined

time

interval,

it

assumes

to

be

out

of

cluster

borders

and

will

try

to

send

its

information

directly

to

CC

(if

possible)
.



Tracking Phase

•
Tracking

includes

taking

continuous

video

of

target

and

sending

video

data

and

location

information

of

target

to

CC

during

specified

time

intervals
.


•
CMs

send

their

data

to

CH

and

CH

is

responsible

to

inform

CC

about

target’s

location
.


•
In

case

CM

goes

out

of

cluster

boundaries

(and

does

not

have

access

to

CH),

it

should

send

the

latest

information

to

CC
.


Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Conclusion
•
Introduced

algorithm

aims

to

improve

cluster

performance

by

making

stable

and

long

living

cluster
.


•
The

stability

of

this

algorithm

is

mostly

because

of

adding

candidate

cluster

members

which

are

highly

probable

of

detecting

target

in

near

future
.


•
The

TFP

value

is

used

as

an

evaluation

value

to

compare

movement

pattern

of

vehicles

with

target
.


•
The

idea

of

distributed

cluster

head

selection

is

introduced

by

use

of

TFP
.